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Record W2209246326 · doi:10.22329/celt.v7i2.3964

Engaging Undergraduates in Social Science Research: The Taking the Pulse of Saskatchewan Project

2014· article· en· W2209246326 on OpenAlexafffundvenueabout
Loleen Berdahl

Bibliographic record

VenueCollected Essays on Learning and Teaching · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsUniversity of Saskatchewan
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Saskatchewan
KeywordsUndergraduate researchGraduate studentsWork (physics)PsychologyData collectionMathematics educationPedagogyMedical educationSociologySocial scienceEngineeringMedicine

Abstract

fetched live from OpenAlex

Although student involvement in research and inquiry can advance undergraduate learning, there are limited opportunities for undergraduate students to be directly involved in social science research. Social science faculty typically work outside of laboratory settings, with the limited research assistance work being completed by graduate students. The challenge, therefore, is to find ways to involve undergraduate social science students in research projects that relate to their field of study and that allow them to develop career-relevant skills. The Taking the Pulse of Saskatchewan project presents a unique model for undergraduate student involvement in social science research. The project involved over 30 faculty members from across the University of Saskatchewan’s Division of Social Sciences and over 40 undergraduate students. Students were directly involved in data collection, data analysis, report writing and media communications. Through their involvement, students developed and applied research, inquiry and communications skills. Drawing on academic literature regarding undergraduate involvement in research, this paper will present an overview of the Taking the Pulse project, and reflect on the lessons of the project.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.055
metaresearch head score (Gemma)0.044
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.268
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0550.044
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0130.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.175
GPT teacher head0.474
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2014
Admission routes4
Has abstractyes

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